Introduire le changement dans les systèmes de soins au Québec : comment tirer profit de l'expérimentation sociale ?
Bibliographic record
Abstract
Introducing change in health care Systems from Quebec: how to benefit from social experimentation? Based on an implementation analysis of an experimentation of a System of integrated care for frail elderly (in the province of Quebec, Canada), this paper initiates reflections about the introduction of reforms in health care Systems. It highlights the advantages of decentralized, participatory change stratégies, based on social experimentation. Theses strategies are particularly interesting in the case of complex changes, in which they in fact allow the autonomy necessary for learning. In conclusion, we propose some hypotheses concerning factors which make these social experimentations successful.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".